import numpy as np
from cmrc_eval import evaluate_cmrc
import collections
from datasets import load_dataset, DatasetDict
from transformers import DefaultDataCollator
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, Trainer, TrainingArguments


def process_func(examples):
    """
    我们假设原有的datasets数据集有三个集合 train val test
    每一个数据集合中 都有多个样本，每一个样本有  id question context answer 四个内容
    在 batched=True的情况下，我们每次取得1000个样本（examples）进行处理。
    在处理过程中将分别对 train 数据集 val数据集 test数据集进行处理。
    假设首先取得train 中的1000条数据。
    这些数据可以 通过 取得 id questions context answer 得到各自的信息列表
    如果 batched=False 则每次取得的数据是单一数据进行处理。
    """
    tokenizer_examples = tokenizer(
        text=examples["question"],
        text_pair=examples["context"],
        return_offsets_mapping=True,
        # 增加滑动窗口
        return_overflowing_tokens=True,
        # 滑动窗口中下一行保留上一个句子中多少个字符，默认是0，也就是不保留
        stride=128,
        max_length=384,
        padding="max_length",
        truncation="only_second"
    )
    print(tokenizer_examples.keys())
    """
    tokenizer_examples 将正式处理训练集中的每条数据，如果batched==True 则每次并行处理1000条数据，否则每次只处理其中一条数据。
    处理的数据包含 'input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'overflow_to_sample_mapping' 等等
    """
    # 将 1000条数据的字典中弹出 overflow_to_sample_mapping 数据 sample_mapping 它的长度是 1827
    sample_mapping = tokenizer_examples.pop("overflow_to_sample_mapping")
    start_positions = []
    end_positions = []
    examples_ids = []
    """
    由于采用了滑动窗口技术，因此 len(sample_mapping) 在第一个批次处理时候 不是1000 而是 1827 
    这说明了有些样本被拆分成了多个，并且赋予了相同的id
    overflow_to_sample_mapping 信息记录了1827个样本其原始1000个样本的id号映射关系是什么
    """
    for idx, _ in enumerate(sample_mapping):
        """
        sample_mapping 记录了1827个拆分后的样本和原始的1000个样本的索引对应关系。
        因此要取得问题的答案，只需要找到对应的索引就行了
        假设idx =  1800  则首先取得 sample_mapping[1800]个拆分样本，
        其对应的id号假设是900，则取得examples['answers'][900]作为改题目的答案
        """
        awswer = examples['answers'][sample_mapping[idx]]
        """
        每一个 awswer 是拆分前样本的问题信息 ，包含了 answer_start 和 text内容
        answer_start 指的是每一个原始样本的答案开头的字符位置
        text 则代表答案内容，取得其长度在加上开头位置，即可以确定答案的结束位置。
        """
        start_char = awswer['answer_start'][0]
        end_char = start_char + len(awswer['text'][0])
        # content_start是指 采用sequence_ids(idx) 得到单个样本中首次出现 1的位置
        # sequence_ids 指的是样本的序列标签，包含了开头部分的多个None值
        """
        sequence_ids 包含了
        首个 None 代表开头特殊字符 
        若干个0 代表问题 
        中间一个None 代表问题和content的中间间隔
        若干个1 代表content信息
        若干个None 代表内容pad补全，包含无意义的信息以及最后的结束符。
        [None, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, None, 
        1, 1, 1, 1, 1, 1, 1, 1, 1 .......
        None, None, None, None, None,.............., None, None, None, None, None]
        """
        # 要取得content的开头token索引位置，需要找到第一个1的位置
        content_start = tokenizer_examples.sequence_ids(idx).index(1)
        # 要取得content的结尾token索引位置，需要从content开头的索引位置开始搜索，一直找到第一个None的位置。
        content_end = tokenizer_examples.sequence_ids(idx).index(None, content_start) - 1
        # offset 指代在所有的token中每一个token的字符位置，由于offset和sequence_ids是一一对应的
        # 又找到了content在sequence_ids的位置，也就是说找到了 content 在offset 的位置。而offset 和字符位置相关
        # 这样我们就可以通过offset 和 content 再结合答案开头和结尾的字符位置，把token位置给夹出来。
        # 有了答案在token中的位置，我们就可以对token做标记，将开头和结尾的token进行标注。
        offset = tokenizer_examples.get('offset_mapping')[idx]
        if offset[content_end][1] < start_char or offset[content_start][0] > end_char:
            # print("答案不在范围内")
            start_token_pos = 0
            end_token_pos = 0
        else:
            token_id = content_start
            while token_id <= content_end and offset[token_id][0] < start_char:
                token_id += 1
            start_token_pos = token_id
            token_id = content_end
            while token_id >= content_start and offset[token_id][1] > end_char:
                token_id -= 1
            end_token_pos = token_id

        start_positions.append(start_token_pos)
        end_positions.append(end_token_pos)
        examples_ids.append(examples['id'][sample_mapping[idx]])
        # 预测时候使用
        tokenizer_examples['offset_mapping'][idx] = [
            (o if tokenizer_examples.sequence_ids(idx)[k] == 1 else None)
            for k, o in enumerate(tokenizer_examples['offset_mapping'][idx])
        ]

    tokenizer_examples["examples_ids"] = examples_ids
    tokenizer_examples["start_positions"] = start_positions
    tokenizer_examples["end_positions"] = end_positions
    """
    print(tokenizer_examples.keys())
    数据处理前
    ['input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'overflow_to_sample_mapping']
    数据处理中
    将 无用的 overflow_to_sample_mapping 弹出来
    将 offset_mapping 进行重构只留下1的部分（内容部分，剩余都是None不参与计算）
    增加 examples_ids 记录样本原始ID
    数据处理后
    dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'examples_ids', 'start_positions', 'end_positions'])
    input_ids id 列表 101 102 为特殊分隔字符 0为 pad [101, 5745, 2455, 7563, 3221, 784, 720, 3198, 952, 6158, 818, 711, 712, 3136, 4638, 8043, 102, 3189, 3091, 1285, 5745, 2455, 7563, 711, 1921, 712, 3136, 3777, 1079, 2600, 3136, 1277, 2134, 2429, 5392, 4415, 809, 1856, 6133, 6421, 3136, 1277, 2600, 712, 3136, 4638, 4958, 5375, 511, 8447, 2399, 124, 3299, 8133, 3189, 8024, 5745, 2455, 7563, 6158, 3136, 2134, 5735, 3307, 924, 4882, 753, 686, 3091, 1285, 711, 1921, 712, 3136, 3777, 1079, 2600, 3136, 1277, 2600, 712, 3136, 2400, 1076, 1921, 712, 3136, 6446, 2255, 3136, 1277, 2134, 2429, 5392, 4415, 8039, 1398, 2399, 8111, 3299, 8153, 3189, 8024, 5735, 3307, 924, 4882, 753, 686, 3091, 1285, 5745, 2455, 7563, 711, 3364, 3322, 511, 5745, 2455, 7563, 1762, 8396, 2399, 5635, 8285, 2399, 3309, 7313, 1139, 818, 1921, 712, 3136, 6632, 1298, 712, 3136, 1730, 712, 2375, 511, 8263, 2399, 125, 3299, 8153, 3189, 8024, 3136, 2134, 5735, 3307, 924, 4882, 753, 686, 818, 1462, 1921, 712, 3136, 6446, 2255, 3136, 1277, 1076, 1921, 712, 3136, 7770, 2398, 3136, 1277, 1426, 1045, 3345, 712, 3136, 711, 1921, 712, 3136, 3777, 1079, 2600, 3136, 1277, 5392, 4415, 712, 3136, 8039, 1350, 5635, 8232, 2399, 123, 3299, 8131, 3189, 8024, 5745, 2455, 7563, 1728, 5815, 2821, 6791, 1343, 2600, 712, 3136, 5466, 1218, 5445, 5783, 828, 8039, 1426, 1045, 3345, 1398, 3189, 4696, 7370, 1921, 712, 3136, 3777, 1079, 2600, 3136, 1277, 2600, 712, 3136, 5466, 1218, 511, 5745, 2455, 7563, 754, 8170, 2399, 123, 3299, 8130, 3189, 3926, 3247, 1762, 3777, 1079, 4895, 686, 8024, 775, 2399, 8426, 2259, 8039, 1071, 5873, 4851, 754, 1398, 3299, 8153, 3189, 677, 1286, 1762, 1921, 712, 3136, 3777, 1079, 2600, 3136, 1277, 2600, 712, 3136, 2429, 1828, 715, 6121, 511, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    token_type_ids 只有二分类信息，不全面 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    attention_mask 只有是否是pad的信息  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    offset_mapping 取得内容有效信息 [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, (516, 517), (517, 518), (518, 519), (519, 520), (520, 521), (521, 522), (522, 523), (523, 524), (524, 525), (525, 526), (526, 527), (527, 528), (528, 529), (529, 530), (530, 531), (531, 532), (532, 533), (533, 534), (534, 535), (535, 536), (536, 537), (537, 538), (538, 539), (539, 540), (540, 541), (541, 542), (542, 543), (543, 544), (544, 545), (545, 546), (546, 547), (547, 548), (548, 552), (552, 553), (553, 554), (554, 555), (555, 557), (557, 558), (558, 559), (559, 560), (560, 561), (561, 562), (562, 563), (563, 564), (564, 565), (565, 566), (566, 567), (567, 568), (568, 569), (569, 570), (570, 571), (571, 572), (572, 573), (573, 574), (574, 575), (575, 576), (576, 577), (577, 578), (578, 579), (579, 580), (580, 581), (581, 582), (582, 583), (583, 584), (584, 585), (585, 586), (586, 587), (587, 588), (588, 589), (589, 590), (590, 591), (591, 592), (592, 593), (593, 594), (594, 595), (595, 596), (596, 597), (597, 598), (598, 599), (599, 600), (600, 601), (601, 603), (603, 604), (604, 606), (606, 607), (607, 608), (608, 609), (609, 610), (610, 611), (611, 612), (612, 613), (613, 614), (614, 615), (615, 616), (616, 617), (617, 618), (618, 619), (619, 620), (620, 621), (621, 622), (622, 623), (623, 624), (624, 625), (625, 626), (626, 627), (627, 631), (631, 632), (632, 633), (633, 637), (637, 638), (638, 639), (639, 640), (640, 641), (641, 642), (642, 643), (643, 644), (644, 645), (645, 646), (646, 647), (647, 648), (648, 649), (649, 650), (650, 651), (651, 652), (652, 653), (653, 657), (657, 658), (658, 659), (659, 660), (660, 662), (662, 663), (663, 664), (664, 665), (665, 666), (666, 667), (667, 668), (668, 669), (669, 670), (670, 671), (671, 672), (672, 673), (673, 674), (674, 675), (675, 676), (676, 677), (677, 678), (678, 679), (679, 680), (680, 681), (681, 682), (682, 683), (683, 684), (684, 685), (685, 686), (686, 687), (687, 688), (688, 689), (689, 690), (690, 691), (691, 692), (692, 693), (693, 694), (694, 695), (695, 696), (696, 697), (697, 698), (698, 699), (699, 700), (700, 701), (701, 702), (702, 703), (703, 704), (704, 705), (705, 706), (706, 707), (707, 708), (708, 709), (709, 710), (710, 714), (714, 715), (715, 716), (716, 717), (717, 719), (719, 720), (720, 721), (721, 722), (722, 723), (723, 724), (724, 725), (725, 726), (726, 727), (727, 728), (728, 729), (729, 730), (730, 731), (731, 732), (732, 733), (733, 734), (734, 735), (735, 736), (736, 737), (737, 738), (738, 739), (739, 740), (740, 741), (741, 742), (742, 743), (743, 744), (744, 745), (745, 746), (746, 747), (747, 748), (748, 749), (749, 750), (750, 751), (751, 752), (752, 753), (753, 754), (754, 755), (755, 756), (756, 757), (757, 758), (758, 759), (759, 760), (760, 761), (761, 762), (762, 763), (763, 767), (767, 768), (768, 769), (769, 770), (770, 772), (772, 773), (773, 774), (774, 775), (775, 776), (776, 777), (777, 778), (778, 779), (779, 780), (780, 781), (781, 782), (782, 783), (783, 785), (785, 786), (786, 787), (787, 788), (788, 789), (789, 790), (790, 791), (791, 792), (792, 793), (793, 795), (795, 796), (796, 797), (797, 798), (798, 799), (799, 800), (800, 801), (801, 802), (802, 803), (803, 804), (804, 805), (805, 806), (806, 807), (807, 808), (808, 809), (809, 810), (810, 811), (811, 812), (812, 813), (813, 814), (814, 815), None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]
    examples_ids 样本id TRAIN_186_QUERY_0
    start_positions 内容答案起始位置 122
    end_positions 内容答案结束位置 128
    """
    return tokenizer_examples


def get_result(start_logits, end_logits, examples, features):
    predictions = {}
    references = {}
    example_to_feature = collections.defaultdict(list)
    # 将features 进行字典映射，以 样本ID为Key idx 列表为 value
    for idx, example_id in enumerate(features["examples_ids"]):
        example_to_feature[example_id].append(idx)

    # 最优答案的优选
    n_best = 20
    max_answer_length = 30
    # 循环每一个验证样本
    for example in examples:
        example_id = example["id"]
        context = example["context"]
        answers = []
        """
        start_logits （样本数 384）
        end_logits（样本数 384）

        单个样本中 feature_idx 可以是多个，代表被拆分后的样本id 对应经过模型后的  start_logits和end_logits 的 id
        """
        # 在一道题目中找到最好的答案
        for feature_idx in example_to_feature[example_id]:
            # start_logit 和 end_logit 长度分别为 384
            start_logit = start_logits[feature_idx]
            end_logit = end_logits[feature_idx]
            # print("start_logit",start_logit)
            # print("end_logit", end_logit)
            # 拿到相应样本的offset_mapping 信息
            offset = features[feature_idx]["offset_mapping"]
            # 拿到 所有的384个数中最大个20个数 相当于排序后取前20
            start_indexes = np.argsort(start_logit)[::-1][:n_best].tolist()
            # 拿到 所有的384个数中最大个20个数
            end_indexes = np.argsort(end_logit)[::-1][:n_best].tolist()
            for start_index in start_indexes:
                for end_index in end_indexes:
                    # 判断如果选出来的位置是None就忽略
                    if offset[start_index] is None or offset[end_index] is None:
                        continue
                    # 判断end小于start的属于非正常信息忽略，或者取得的答案长度超过最长的回答长度的也忽略
                    if end_index < start_index or end_index - start_index + 1 > max_answer_length:
                        continue
                    # 添加回答，和每个回答的分数
                    answers.append(
                        {"text": context[offset[start_index][0]:offset[end_index][1]],
                         "score": start_logit[start_index] + end_logit[end_index]}
                    )
        if len(answers) > 0:
            """
              由于之前样本做过切分，因此一道题目可能变成多个小题目（滑动窗口方式），
              如果回答大于1 说明在多个小题中找到多个答案，这里要选择分数最高的答案
            """
            best_answer = max(answers, key=lambda x: x["score"])
            predictions[example_id] = best_answer["text"]
        else:
            # 没有找到答案，说明这个题目没有答案
            predictions[example_id] = ""
        # 针对有答案的题目我们给答案，有些数据集合（如测试集）没有答案，因此这里给空字符串
        if "answers" in example:
            references[example_id] = example["answers"]["text"]
        else:
            references[example_id] = ""
    return predictions, references


from transformers import BertForQuestionAnswering
from transformers.trainer_utils import EvalPrediction


def compute_metrics(preds):
    """
    from transformers import BertForQuestionAnswering
    1、经过bert模型之后的输出 outputs[0].shape torch.Size([2, 384, 768])
    2、经过一个线性层将 torch.Size([2, 384, 768] 变成 torch.Size([2, 384, 2]
    2、拆分结果 start_logits [2（batch_size）, 384(文本长度)] 标签  start_positions [2]
    3、end_logits [2（batch_size）, 384(文本长度)] 标签  end_positions [2]
    4、将 start_logits 的 2*384的特征和标签 start_positions【2】进行 CrossEntropyLoss计算
      将 end_logits 的 2*384的特征和标签 end_positions【2】进行 CrossEntropyLoss计算
      开头和结尾都算一下。最后 total_loss 只计算答案开头和结尾位置的 loss（开头结尾loss 平均值。)
    5、输出结果 loss start_logits tensor(5.6042,)
      输出结果 start_logits torch.Size([2, 384])
      输出结果 end_logits torch.Size([2, 384])
    """
    """
    from transformers.trainer_utils import EvalPrediction
    preds（EvalPrediction类） 分成三个属性 1、predictions 2、label_ids 3、inputs
    且可进行列表方式访问 preds[0] predictions preds[1] label_ids preds[2] inputs
    label_ids 是指的开头和结尾位置的列表组合，用于评估数据集长度为6327
    因此 label_ids 是一个开头列表（长度为 6327）和结尾列表（长度也为 6327）组成的长度为2的数组
    predictions 则是一个 6327*384 的开头列表和 6327*384 的结尾列表
    """
    # 拿到输出中的 start_logits end_logits 对应模型输出后的 start_logits 和 end_logits
    start_logits, end_logits = preds[0]
    if start_logits.shape[0] == len(tokenized_datasets["validation"]):
        """
        start_logits (6327, 384) end_logits (6327, 384)

        datasets["validation"]
            Dataset({
            features: ['id', 'context', 'question', 'answers'],
            num_rows: 3219
        })

        tokenized_datasets["test"]
        Dataset({
            features: ['input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'examples_ids', 'start_positions', 'end_positions'],
            num_rows: 1988
        })
        """
        p, r = get_result(start_logits, end_logits, datasets["validation"], tokenized_datasets["validation"])
    else:
        p, r = get_result(start_logits, end_logits, datasets["test"], tokenized_datasets["test"])
    # 拿到 predictions, references 一个是计算得到的信息，一个是label信息，（测试集没有label）
    return evaluate_cmrc(p, r)


datasets = DatasetDict.load_from_disk('/data/datasets/cmrc2018')
tokenizer = AutoTokenizer.from_pretrained("/data/models/huggingface/chinese-macbert-base")
# tokenized_datasets = datasets.map(process_func, remove_columns=datasets['train'].column_names)
tokenized_datasets = datasets.map(process_func, batched=True, remove_columns=datasets['train'].column_names)

print(tokenized_datasets['train'][0:4])
"""
# 命名实体识别
{'id': ['0', '1', '2', '3'], 
'tokens': [['海', '钓', '比', '赛', '地', '点', '在', '厦', '门', '与', '金', '门', '之', '间', '的', '海', '域', '。'], ['这', '座', '依', '山', '傍', '水', '的', '博', '物', '馆', '由', '国', '内', '一', '流', '的', '设', '计', '师', '主', '持', '设', '计', '，', '整', '个', '建', '筑', '群', '精', '美', '而', '恢', '宏', '。'], ['但', '作', '为', '一', '个', '共', '产', '党', '员', '、', '人', '民', '公', '仆', '，', '应', '当', '胸', '怀', '宽', '阔', '，', '真', '正', '做', '到', '“', '先', '天', '下', '之', '忧', '而', '忧', '，', '后', '天', '下', '之', '乐', '而', '乐', '”', '，', '淡', '化', '个', '人', '的', '名', '利', '得', '失', '和', '宠', '辱', '悲', '喜', '，', '把', '改', '革', '大', '业', '摆', '在', '首', '位', '，', '这', '样', '才', '能', '超', '越', '自', '我', '，', '摆', '脱', '世', '俗', '，', '有', '所', '作', '为', '。'], ['在', '发', '达', '国', '家', '，', '急', '救', '保', '险', '十', '分', '普', '及', '，', '已', '成', '为', '社', '会', '保', '障', '体', '系', '的', '重', '要', '组', '成', '部', '分', '。']], 
'ner_tags': [[0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 
'input_ids': [[101, 3862, 7157, 3683, 6612, 1765, 4157, 1762, 1336, 7305, 680, 7032, 7305, 722, 7313, 4638, 3862, 1818, 511, 102], [101, 6821, 2429, 898, 2255, 988, 3717, 4638, 1300, 4289, 7667, 4507, 1744, 1079, 671, 3837, 4638, 6392, 6369, 2360, 712, 2898, 6392, 6369, 8024, 3146, 702, 2456, 5029, 5408, 5125, 5401, 5445, 2612, 2131, 511, 102], [101, 852, 868, 711, 671, 702, 1066, 772, 1054, 1447, 510, 782, 3696, 1062, 789, 8024, 2418, 2496, 5541, 2577, 2160, 7333, 8024, 4696, 3633, 976, 1168, 100, 1044, 1921, 678, 722, 2569, 5445, 2569, 8024, 1400, 1921, 678, 722, 727, 5445, 727, 100, 8024, 3909, 1265, 702, 782, 4638, 1399, 1164, 2533, 1927, 1469, 2143, 6802, 2650, 1599, 8024, 2828, 3121, 7484, 1920, 689, 3030, 1762, 7674, 855, 8024, 6821, 3416, 2798, 5543, 6631, 6632, 5632, 2769, 8024, 3030, 5564, 686, 921, 8024, 3300, 2792, 868, 711, 511, 102], [101, 1762, 1355, 6809, 1744, 2157, 8024, 2593, 3131, 924, 7372, 1282, 1146, 3249, 1350, 8024, 2347, 2768, 711, 4852, 833, 924, 7397, 860, 5143, 4638, 7028, 6206, 5299, 2768, 6956, 1146, 511, 102]], 
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
'labels': [[-100, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]}
DataCollatorForTokenClassification(tokenizer)

# 阅读理解
{'input_ids': [[101, 5745, 2455, 7563, 3221, 784, 720, 3198, 952, 6158, 818, 711, 712, 3136, 4638, 8043, 102, 5745, 2455, 7563, 3364, 3322, 8020, 8024, 8021, 8024, 1760, 1399, 924, 4882, 185, 5735, 4449, 8020, 8021, 8024, 3221, 6632, 1298, 5384, 7716, 1921, 712, 3136, 3364, 3322, 511, 9155, 2399, 6158, 818, 711, 712, 3136, 8039, 8431, 2399, 6158, 3091, 1285, 711, 1921, 712, 3136, 3777, 1079, 2600, 3136, 1277, 2134, 2429, 5392, 4415, 8039, 8447, 2399, 6158, 3091, 1285, 711, 2600, 712, 3136, 8024, 1398, 2399, 2399, 2419, 6158, 3091, 1285, 711, 3364, 3322, 8039, 8170, 2399, 123, 3299, 4895, 686, 511, 5745, 2455, 7563, 754, 9915, 2399, 127, 3299, 8115, 3189, 1762, 6632, 1298, 2123, 2398, 4689, 1921, 712, 3136, 1355, 5683, 3136, 1277, 1139, 4495, 8039, 4997, 2399, 3198, 2970, 1358, 5679, 1962, 3136, 5509, 1400, 8024, 6158, 671, 855, 6632, 1298, 4868, 4266, 2372, 1168, 3777, 1079, 5326, 5330, 1071, 2110, 689, 511, 5745, 2455, 7563, 754, 9211, 2399, 1762, 3777, 1079, 1920, 934, 6887, 7368, 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3136, 8024, 1398, 2399, 129, 3299, 8115, 3189, 2218, 818, 8039, 1071, 4288, 7208, 711, 519, 2769, 928, 1921, 712, 4638, 4263, 520, 511, 4507, 754, 5745, 2455, 7563, 6158, 6632, 1298, 3124, 2424, 6763, 4881, 2345, 679, 1914, 8114, 2399, 8024, 1728, 3634, 800, 3187, 3791, 1168, 2792, 2247, 1828, 1277, 6822, 6121, 4288, 4130, 2339, 868, 5445, 683, 3800, 4777, 6438, 5023, 2339, 868, 511, 5745, 2455, 7563, 7370, 749, 7481, 2190, 2773, 751, 510, 6577, 1737, 510, 6158, 2496, 2229, 6833, 2154, 1921, 712, 3136, 833, 5023, 7309, 7579, 1912, 8024, 738, 4908, 2166, 2612, 1908, 934, 7368, 510, 1158, 2456, 1957, 934, 833, 1730, 860, 5023, 511, 8431, 2399, 8024, 3136, 2134, 5735, 3307, 924, 4882, 753, 686, 1762, 1398, 2399, 127, 3299, 8123, 3189, 3091, 1285, 5745, 2455, 7563, 711, 1921, 712, 3136, 3777, 1079, 2600, 3136, 1277, 2134, 2429, 5392, 4415, 809, 1856, 6133, 6421, 3136, 1277, 2600, 712, 3136, 4638, 4958, 5375, 511, 8447, 2399, 124, 3299, 8133, 3189, 8024, 5745, 2455, 7563, 6158, 3136, 2134, 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[312, 313], [313, 314], [314, 315], [315, 316], [316, 317], [317, 318], [318, 319], [319, 320], [320, 321], [321, 325], [325, 326], [326, 327], [327, 328], [328, 329], [329, 330], [330, 331], [331, 332], [332, 333], [333, 334], [334, 335], [335, 336], [336, 337], [337, 338], [338, 339], [339, 340], [340, 341], [341, 342], [342, 343], [343, 344], [344, 345], [345, 346], [346, 347], [347, 348], [348, 349], [349, 350], [350, 351], [351, 352], [352, 353], [353, 354], [354, 355], [355, 356], [356, 360], [360, 361], [361, 362], [362, 363], [363, 364], [364, 365], [365, 366], [366, 367], [367, 368], [368, 369], [369, 370], [370, 371], [371, 372], [372, 373], [373, 374], [374, 375], [375, 376], [376, 377], [377, 378], [378, 379], [379, 380], [380, 381], [381, 382], [382, 383], [383, 384], [384, 385], [385, 386], [386, 387], [387, 388], [388, 390], [390, 391], [391, 392], [392, 393], [393, 394], [394, 395], [395, 396], [396, 397], [397, 398], [398, 399], [399, 400], [400, 401], [401, 402], 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[577, 578], [578, 579], [579, 580], [580, 581], [581, 582], [582, 583], [583, 584], [584, 585], [585, 586], [586, 587], [587, 588], [588, 589], [589, 590], [590, 591], [591, 592], [592, 593], [593, 594], [594, 595], [595, 596], [596, 597], [597, 598], [598, 599], [599, 600], [600, 601], [601, 603], [603, 604], [604, 606], [606, 607], [607, 608], [608, 609], [609, 610], [610, 611], [611, 612], [612, 613], [613, 614], [614, 615], [615, 616], [616, 617], [617, 618], [618, 619], [619, 620], [620, 621], [621, 622], [622, 623], [623, 624], [624, 625], [625, 626], [626, 627], [627, 631], [631, 632], [632, 633], [633, 637], [637, 638], [638, 639], [639, 640], [640, 641], [641, 642], [642, 643], [643, 644], [644, 645], [645, 646], [646, 647], [647, 648], [648, 649], [649, 650], [650, 651], [651, 652], [652, 653], [653, 657], [657, 658], [658, 659], None], [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, [516, 517], [517, 518], [518, 519], [519, 520], [520, 521], [521, 522], [522, 523], [523, 524], [524, 525], [525, 526], [526, 527], [527, 528], [528, 529], [529, 530], [530, 531], [531, 532], [532, 533], [533, 534], [534, 535], [535, 536], [536, 537], [537, 538], [538, 539], [539, 540], [540, 541], [541, 542], [542, 543], [543, 544], [544, 545], [545, 546], [546, 547], [547, 548], [548, 552], [552, 553], [553, 554], [554, 555], [555, 557], [557, 558], [558, 559], [559, 560], [560, 561], [561, 562], [562, 563], [563, 564], [564, 565], [565, 566], [566, 567], [567, 568], [568, 569], [569, 570], [570, 571], [571, 572], [572, 573], [573, 574], [574, 575], [575, 576], [576, 577], [577, 578], [578, 579], [579, 580], [580, 581], [581, 582], [582, 583], [583, 584], [584, 585], [585, 586], [586, 587], [587, 588], [588, 589], [589, 590], [590, 591], [591, 592], [592, 593], [593, 594], [594, 595], [595, 596], [596, 597], [597, 598], [598, 599], [599, 600], [600, 601], [601, 603], [603, 604], [604, 606], [606, 607], [607, 608], [608, 609], [609, 610], [610, 611], [611, 612], [612, 613], [613, 614], [614, 615], [615, 616], [616, 617], [617, 618], [618, 619], [619, 620], [620, 621], [621, 622], [622, 623], [623, 624], [624, 625], [625, 626], [626, 627], [627, 631], [631, 632], [632, 633], [633, 637], [637, 638], [638, 639], [639, 640], [640, 641], [641, 642], [642, 643], [643, 644], [644, 645], [645, 646], [646, 647], [647, 648], [648, 649], [649, 650], [650, 651], [651, 652], [652, 653], [653, 657], [657, 658], [658, 659], [659, 660], [660, 662], [662, 663], [663, 664], [664, 665], [665, 666], [666, 667], [667, 668], [668, 669], [669, 670], [670, 671], [671, 672], [672, 673], [673, 674], [674, 675], [675, 676], [676, 677], [677, 678], [678, 679], [679, 680], [680, 681], [681, 682], [682, 683], [683, 684], [684, 685], [685, 686], [686, 687], [687, 688], [688, 689], [689, 690], [690, 691], [691, 692], [692, 693], [693, 694], [694, 695], [695, 696], [696, 697], [697, 698], [698, 699], [699, 700], [700, 701], 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[793, 795], [795, 796], [796, 797], [797, 798], [798, 799], [799, 800], [800, 801], [801, 802], [802, 803], [803, 804], [804, 805], [805, 806], [806, 807], [807, 808], [808, 809], [809, 810], [810, 811], [811, 812], [812, 813], [813, 814], [814, 815], None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None], [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13], [13, 14], [14, 15], [15, 16], [16, 17], [17, 18], [18, 19], [19, 20], [20, 21], [21, 22], [22, 23], [23, 24], [24, 25], [25, 26], [26, 27], [27, 28], [28, 29], [29, 30], [30, 34], [34, 35], [35, 36], [36, 37], [37, 38], [38, 39], [39, 40], [40, 41], [41, 45], [45, 46], [46, 47], [47, 48], [48, 49], [49, 50], [50, 51], [51, 52], [52, 53], [53, 54], [54, 55], [55, 56], [56, 57], [57, 58], [58, 59], [59, 60], [60, 61], [61, 62], [62, 63], [63, 67], [67, 68], [68, 69], [69, 70], [70, 71], [71, 72], [72, 73], [73, 74], [74, 75], [75, 76], [76, 77], [77, 78], [78, 79], [79, 80], [80, 81], [81, 82], [82, 83], [83, 84], [84, 85], [85, 86], [86, 87], [87, 91], [91, 92], [92, 93], [93, 94], [94, 95], [95, 96], [96, 97], [97, 98], [98, 99], [99, 100], [100, 101], [101, 105], [105, 106], [106, 107], [107, 108], [108, 110], [110, 111], [111, 112], [112, 113], [113, 114], [114, 115], [115, 116], [116, 117], [117, 118], [118, 119], [119, 120], [120, 121], [121, 122], [122, 123], [123, 124], [124, 125], [125, 126], [126, 127], [127, 128], [128, 129], [129, 130], [130, 131], [131, 132], [132, 133], [133, 134], [134, 135], [135, 136], [136, 137], [137, 138], [138, 139], [139, 140], [140, 141], [141, 142], [142, 143], [143, 144], [144, 145], [145, 146], [146, 147], [147, 148], [148, 149], [149, 150], [150, 151], [151, 152], [152, 153], [153, 154], [154, 155], [155, 156], [156, 157], [157, 158], [158, 159], [159, 163], [163, 164], [164, 165], [165, 166], [166, 167], [167, 168], [168, 169], [169, 170], [170, 171], [171, 172], [172, 173], [173, 174], [174, 175], [175, 176], [176, 177], [177, 178], [178, 179], [179, 180], [180, 181], [181, 182], [182, 186], [186, 187], [187, 188], [188, 189], [189, 190], [190, 191], [191, 192], [192, 193], [193, 194], [194, 195], [195, 196], [196, 197], [197, 198], [198, 199], [199, 200], [200, 201], [201, 202], [202, 203], [203, 204], [204, 205], [205, 206], [206, 207], [207, 208], [208, 209], [209, 210], [210, 211], [211, 212], [212, 213], [213, 214], [214, 215], [215, 216], [216, 217], [217, 218], [218, 222], [222, 223], [223, 224], [224, 225], [225, 226], [226, 227], [227, 228], [228, 229], [229, 230], [230, 231], [231, 232], [232, 233], [233, 234], [234, 235], [235, 236], [236, 237], [237, 238], [238, 239], [239, 240], [240, 241], [241, 242], [242, 243], [243, 244], [244, 245], [245, 246], [246, 247], [247, 248], [248, 249], [249, 250], [250, 251], [251, 252], [252, 253], [253, 257], [257, 258], [258, 259], [259, 260], [260, 261], [261, 262], [262, 263], [263, 264], [264, 265], [265, 266], [266, 267], [267, 268], [268, 269], [269, 270], [270, 271], [271, 272], [272, 273], [273, 274], [274, 275], [275, 276], [276, 277], [277, 278], [278, 279], [279, 280], [280, 281], [281, 282], [282, 283], [283, 284], [284, 285], [285, 286], [286, 287], [287, 288], [288, 289], [289, 290], [290, 291], [291, 292], [292, 293], [293, 294], [294, 295], [295, 296], [296, 297], [297, 298], [298, 299], [299, 300], [300, 301], [301, 302], [302, 303], [303, 304], [304, 305], [305, 306], [306, 307], [307, 308], [308, 309], [309, 310], [310, 311], [311, 312], [312, 313], [313, 314], [314, 315], [315, 316], [316, 317], [317, 318], [318, 319], [319, 320], [320, 321], [321, 325], [325, 326], [326, 327], [327, 328], [328, 329], [329, 330], [330, 331], [331, 332], [332, 333], [333, 334], [334, 335], [335, 336], [336, 337], [337, 338], [338, 339], [339, 340], [340, 341], [341, 342], [342, 343], [343, 344], [344, 345], [345, 346], [346, 347], [347, 348], [348, 349], [349, 350], [350, 351], [351, 352], [352, 353], [353, 354], [354, 355], [355, 356], [356, 360], [360, 361], [361, 362], [362, 363], [363, 364], [364, 365], [365, 366], [366, 367], [367, 368], [368, 369], [369, 370], [370, 371], [371, 372], [372, 373], [373, 374], [374, 375], [375, 376], [376, 377], [377, 378], [378, 379], [379, 380], [380, 381], [381, 382], [382, 383], [383, 384], [384, 385], [385, 386], [386, 387], [387, 388], [388, 390], [390, 391], [391, 392], [392, 393], [393, 394], [394, 395], [395, 396], [396, 397], [397, 398], [398, 399], [399, 400], [400, 401], [401, 402], [402, 403], None]], 
'examples_ids': ['TRAIN_186_QUERY_0', 'TRAIN_186_QUERY_0', 'TRAIN_186_QUERY_0', 'TRAIN_186_QUERY_1'], 
'start_positions': [47, 0, 0, 53], 
'end_positions': [48, 0, 0, 70]}

# 多项选择题
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 
'labels': [1, 1]}

# 文本相似度
{'input_ids': [[101, 1688, 8024, 843, 4265, 7323, 678, 8013, 102, 2769, 812, 677, 2414, 679, 719, 8024, 4649, 3362, 2990, 1044, 4495, 1469, 3727, 1990, 2218, 1343, 2936, 7824, 749, 511, 102], [101, 2797, 3322, 3905, 2140, 6397, 817, 2582, 720, 4692, 102, 2797, 3322, 3905, 2140, 2582, 720, 3121, 6397, 817, 8024, 102]], 
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
'labels': [0, 0]}
DataCollatorWithPadding(tokenizer=tokenizer)

# 文本相似度2
{'input_ids': [[101, 671, 702, 2785, 4706, 7262, 4638, 782, 6438, 5291, 4813, 4638, 904, 7481, 511, 102, 671, 702, 1957, 782, 1762, 5381, 677, 6370, 2847, 5855, 511, 102], 
[101, 2769, 1762, 7345, 1378, 677, 2482, 678, 5587, 8024, 3633, 6206, 1586, 1586, 1765, 6432, 671, 1898, 2769, 4638, 1921, 886, 100, 100, 809, 671, 4905, 2496, 4197, 1372, 3300, 2658, 782, 2798, 1420, 2533, 6224, 4638, 6427, 6444, 100, 100, 2575, 4197, 1961, 6716, 1400, 4638, 7716, 6756, 7027, 1348, 6663, 678, 671, 702, 782, 3341, 8024, 738, 2847, 4708, 3159, 5076, 102, 677, 935, 4708, 6716, 2094, 8024, 1157, 6206, 856, 1898, 1373, 1542, 100, 671, 4500, 1372, 3300, 2658, 782, 2798, 1420, 2533, 6224, 4638, 6427, 6444, 671, 671, 1316, 3300, 671, 702, 782, 6656, 4708, 1961, 794, 7716, 6756, 7027, 6663, 678, 3341, 8039, 738, 2847, 4708, 2847, 7599, 131, 1377, 3221, 1762, 782, 6121, 6887, 677, 1355, 1139, 1510, 1898, 4638, 102]], 
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
'labels': [0.0, 1.0]}

# 文本相似度3
{'input_ids': [
    [
        [101, 1298, 1092, 2346, 5307, 1086, 738, 3187, 1070, 1377, 1872, 749, 8024, 5445, 1266, 881, 1156, 4385, 2346, 2971, 1169, 749, 794, 4506, 5287, 6205, 2518, 1298, 671, 4684, 1168, 1184, 5296, 4638, 7188, 6662, 8024, 2792, 809, 6468, 2209, 3294, 1921, 1921, 6963, 3300, 3001, 1092, 2458, 1168, 8024, 3300, 5314, 1075, 6133, 1041, 511, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
        [101, 1298, 6956, 5468, 4673, 1092, 2347, 3766, 3300, 2361, 3307, 2533, 1168, 3118, 3001, 749, 8024, 5445, 1266, 3175, 878, 2971, 1169, 4638, 794, 4506, 5287, 6205, 2518, 1298, 4684, 6821, 7347, 1765, 4638, 7188, 6662, 1316, 3975, 3975, 679, 3171, 1765, 5314, 6468, 2209, 3294, 6817, 3341, 3001, 1070, 1469, 5314, 1075, 511, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    ], 
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], 
'token_type_ids': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 
'attention_mask': [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 
'labels': [1, -1]}
"""
# model = AutoModelForQuestionAnswering.from_pretrained("/data/models/huggingface/chinese-macbert-base")
# # print(model)
# training_args = TrainingArguments(
#     output_dir="/data/model_for_qa/output",
#     per_device_train_batch_size=2,
#     per_device_eval_batch_size=2,
#     gradient_accumulation_steps=2,
#     # eval_strategy='epoch',
#     # 20 步做一次评估
#     eval_strategy="steps",
#     eval_steps=20,
#     learning_rate=5e-5,
#     weight_decay=0.01,
#     warmup_ratio=0.1,
#     # metric_for_best_model='f1',
#     # load_best_model_at_end=True,
#     # 多少步打印一下日志信息
#     logging_steps=10,
#     num_train_epochs=5
# )
#
# trainer = Trainer(
#     model=model,
#     args=training_args,
#     train_dataset=tokenized_datasets['train'],
#     eval_dataset=tokenized_datasets['validation'],
#     data_collator=DefaultDataCollator(),
#     compute_metrics=compute_metrics
# )
#
# trainer.train()
#
# from transformers import pipeline
#
# pipe = pipeline("question-answering", model=model, tokenizer=tokenizer, device=0)
# pipe(question="小明在哪里上的飞机？",
#      context="小明去中国，由于定了到北京的机票，所以先坐车去了大阪，然后从大阪上的飞机，坐了飞机去了北京。")
